


"""simple docstring"""



import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)
def      UpperCAmelCase     (       _lowercase :   List[Any]	)						-> List[Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			model.config

	lowerCAmelCase_      							=			DonutSwinConfig(
	    image_size=original_config.input_size			,   patch_size=4			,   depths=original_config.encoder_layer			,   num_heads=[4, 8, 1_6, 3_2]			,   window_size=original_config.window_size			,   embed_dim=1_2_8			,   )
	lowerCAmelCase_      							=			MBartConfig(
	    is_decoder=_lowercase			,   is_encoder_decoder=_lowercase			,   add_cross_attention=_lowercase			,   decoder_layers=original_config.decoder_layer			,   max_position_embeddings=original_config.max_position_embeddings			,   vocab_size=len(
	        model.decoder.tokenizer	)			,   scale_embedding=_lowercase			,   add_final_layer_norm=_lowercase			,   )

	return encoder_config, decoder_config
def      UpperCAmelCase     (       _lowercase :   Any	)						-> Union[str, Any]:


	"""simple docstring"""




	if "encoder.model" in name:
		lowerCAmelCase_      							=			name.replace('''encoder.model'''			,   '''encoder'''	)
	if "decoder.model" in name:
		lowerCAmelCase_      							=			name.replace('''decoder.model'''			,   '''decoder'''	)
	if "patch_embed.proj" in name:
		lowerCAmelCase_      							=			name.replace('''patch_embed.proj'''			,   '''embeddings.patch_embeddings.projection'''	)
	if "patch_embed.norm" in name:
		lowerCAmelCase_      							=			name.replace('''patch_embed.norm'''			,   '''embeddings.norm'''	)
	if name.startswith('''encoder'''	):
		if "layers" in name:
			lowerCAmelCase_      							=			'''encoder.''' + name
		if "attn.proj" in name:
			lowerCAmelCase_      							=			name.replace('''attn.proj'''			,   '''attention.output.dense'''	)
		if "attn" in name and "mask" not in name:
			lowerCAmelCase_      							=			name.replace('''attn'''			,   '''attention.self'''	)
		if "norm1" in name:
			lowerCAmelCase_      							=			name.replace('''norm1'''			,   '''layernorm_before'''	)
		if "norm2" in name:
			lowerCAmelCase_      							=			name.replace('''norm2'''			,   '''layernorm_after'''	)
		if "mlp.fc1" in name:
			lowerCAmelCase_      							=			name.replace('''mlp.fc1'''			,   '''intermediate.dense'''	)
		if "mlp.fc2" in name:
			lowerCAmelCase_      							=			name.replace('''mlp.fc2'''			,   '''output.dense'''	)

		if name == "encoder.norm.weight":
			lowerCAmelCase_      							=			'''encoder.layernorm.weight'''
		if name == "encoder.norm.bias":
			lowerCAmelCase_      							=			'''encoder.layernorm.bias'''

	return name
def      UpperCAmelCase     (       _lowercase :   Any			,   _lowercase :   Tuple	)						-> List[Any]:


	"""simple docstring"""




	for key in orig_state_dict.copy().keys():
		lowerCAmelCase_      							=			orig_state_dict.pop(_lowercase	)

		if "qkv" in key:
			lowerCAmelCase_      							=			key.split('''.'''	)
			lowerCAmelCase_      							=			int(key_split[3]	)
			lowerCAmelCase_      							=			int(key_split[5]	)
			lowerCAmelCase_      							=			model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

			if "weight" in key:
				lowerCAmelCase_      							=			val[:dim, :]
				lowerCAmelCase_      							=			val[dim : dim * 2, :]
				lowerCAmelCase_      							=			val[-dim:, :]
			else:
				lowerCAmelCase_      							=			val[:dim]
				lowerCAmelCase_      							=			val[dim : dim * 2]
				lowerCAmelCase_      							=			val[-dim:]
		elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
			# HuggingFace implementation doesn't use attn_mask buffer
			# and model doesn't use final LayerNorms for the encoder
			pass
		else:
			lowerCAmelCase_      							=			val

	return orig_state_dict
def      UpperCAmelCase     (       _lowercase :   Optional[Any]			,   _lowercase :   Union[str, Any]=None			,   _lowercase :   Any=False	)						-> str:


	"""simple docstring"""




	lowerCAmelCase_      							=			DonutModel.from_pretrained(_lowercase	).eval()

	# load HuggingFace model
	lowerCAmelCase_							,	lowerCAmelCase_      							=			get_configs(_lowercase	)
	lowerCAmelCase_      							=			DonutSwinModel(_lowercase	)
	lowerCAmelCase_      							=			MBartForCausalLM(_lowercase	)
	lowerCAmelCase_      							=			VisionEncoderDecoderModel(encoder=_lowercase			,   decoder=_lowercase	)
	model.eval()

	lowerCAmelCase_      							=			original_model.state_dict()
	lowerCAmelCase_      							=			convert_state_dict(_lowercase			,   _lowercase	)
	model.load_state_dict(_lowercase	)

	# verify results on scanned document
	lowerCAmelCase_      							=			load_dataset('''hf-internal-testing/example-documents'''	)
	lowerCAmelCase_      							=			dataset['''test'''][0]['''image'''].convert('''RGB'''	)

	lowerCAmelCase_      							=			XLMRobertaTokenizerFast.from_pretrained(_lowercase			,   from_slow=_lowercase	)
	lowerCAmelCase_      							=			DonutImageProcessor(
	    do_align_long_axis=original_model.config.align_long_axis			,   size=original_model.config.input_size[::-1]	)
	lowerCAmelCase_      							=			DonutProcessor(_lowercase			,   _lowercase	)
	lowerCAmelCase_      							=			processor(_lowercase			,   return_tensors='''pt'''	).pixel_values

	if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
		lowerCAmelCase_      							=			'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
		lowerCAmelCase_      							=			'''When is the coffee break?'''
		lowerCAmelCase_      							=			task_prompt.replace('''{user_input}'''			,   _lowercase	)
	elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
		lowerCAmelCase_      							=			'''<s_rvlcdip>'''
	elif model_name in [
	    "naver-clova-ix/donut-base-finetuned-cord-v1",
	    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
	]:
		lowerCAmelCase_      							=			'''<s_cord>'''
	elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
		lowerCAmelCase_      							=			'''s_cord-v2>'''
	elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
		lowerCAmelCase_      							=			'''<s_zhtrainticket>'''
	elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
		# use a random prompt
		lowerCAmelCase_      							=			'''hello world'''
	else:
		raise ValueError('''Model name not supported'''	)
	lowerCAmelCase_      							=			original_model.decoder.tokenizer(_lowercase			,   add_special_tokens=_lowercase			,   return_tensors='''pt'''	)[
	    '''input_ids'''
	]

	lowerCAmelCase_      							=			original_model.encoder.model.patch_embed(_lowercase	)
	lowerCAmelCase_							,	lowerCAmelCase_      							=			model.encoder.embeddings(_lowercase	)
	assert torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)

	# verify encoder hidden states
	lowerCAmelCase_      							=			original_model.encoder(_lowercase	)
	lowerCAmelCase_      							=			model.encoder(_lowercase	).last_hidden_state
	assert torch.allclose(_lowercase			,   _lowercase			,   atol=1E-2	)

	# verify decoder hidden states
	lowerCAmelCase_      							=			original_model(_lowercase			,   _lowercase			,   _lowercase	).logits
	lowerCAmelCase_      							=			model(_lowercase			,   decoder_input_ids=_lowercase	).logits
	assert torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Looks ok!'''	)

	if pytorch_dump_folder_path is not None:
		print(F"""Saving model and processor to {pytorch_dump_folder_path}"""	)
		model.save_pretrained(_lowercase	)
		processor.save_pretrained(_lowercase	)

	if push_to_hub:
		model.push_to_hub('''nielsr/''' + model_name.split('''/'''	)[-1]			,   commit_message='''Update model'''	)
		processor.push_to_hub('''nielsr/''' + model_name.split('''/'''	)[-1]			,   commit_message='''Update model'''	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--model_name',
		    default='naver-clova-ix/donut-base-finetuned-docvqa',
		    required=False,
		    type=str,
		    help='Name of the original model you\'d like to convert.',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path',
		    default=None,
		    required=False,
		    type=str,
		    help='Path to the output PyTorch model directory.',
		)
		parser.add_argument(
		    '--push_to_hub',
		    action='store_true',
		    help='Whether or not to push the converted model and processor to the 🤗 hub.',
		)

		lowercase_							=							parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			os.path.join(args.tf_model_dir			,   '''parameters.json'''	)
	lowerCAmelCase_      							=			json.loads(open(_lowercase	).read()	)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
	if not args.output.endswith('''.pt'''	):
		lowerCAmelCase_      							=			args.output + '''.pt'''
	lowerCAmelCase_      							=			OrderedDict()
	with tf.device('''/CPU:0'''	):
		lowerCAmelCase_      							=			tf.train.load_checkpoint(args.tf_model_dir	)
		lowerCAmelCase_      							=			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			lowerCAmelCase_      							=			reader.get_tensor(_lowercase	).astype(np.floataa	)
			if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
				continue
			if key_name.startswith('''pasts/'''	):
				if key_name.startswith('''pasts/mlp'''	):
					lowerCAmelCase_      							=			int(key_name[9]	)
				elif key_name.startswith('''pasts/out'''	):
					lowerCAmelCase_      							=			8
				lowerCAmelCase_      							=			'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/moe'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/switch_gating/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/softmlp/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
					lowerCAmelCase_      							=			key_name[-9:-7]
					for i in range(1_6	):
						lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
						lowerCAmelCase_      							=			(
						    vnp[i].transpose([1, 0]	).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/mlp'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/p1/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p1/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/ln'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/att'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/qkv/kernel'''	):
					lowerCAmelCase_      							=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					lowerCAmelCase_      							=			state[:, 0, :, :]
					lowerCAmelCase_      							=			state[:, 1, :, :]
					lowerCAmelCase_      							=			state[:, 2, :, :]
					lowerCAmelCase_      							=			(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/o/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
					lowerCAmelCase_      							=			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/an'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif (
			    key_name.startswith('''model/wte'''	)
			    or key_name.startswith('''model/wpe'''	)
			    or key_name.startswith('''model/ete'''	)
			):
				lowerCAmelCase_      							=			{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
				    key_name[-3:]
				]
				lowerCAmelCase_      							=			'''model.%s.weight''' % nlayer
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				if key_name.startswith('''model/wte'''	):
					lowerCAmelCase_      							=			'''lm_head.weight'''
					lowerCAmelCase_      							=			vnp.copy()  # same in embedded
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/wob'''	):
				lowerCAmelCase_      							=			'''final_logits_bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			state.reshape((1, -1)	)
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense/kernel":
				lowerCAmelCase_      							=			'''model.last_project.weight'''
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense_1/bias":
				lowerCAmelCase_      							=			'''model.last_project.bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
	torch.save(_lowercase			,   args.output	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser(
		    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
		parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
		lowercase_							=							parser.parse_args()
		convert_tf_gptsan_to_pt(args)